The growing interest in applying Artificial Intelligence (AI) to software engineering has accelerated since the release of ChatGPT 3.5 in 2022. This paper investigates how Large Language Models (LLMs) support applications’ modular development and testing. We introduce AIPyCraft, a novel AI-assisted framework that facilitates the end-to-end lifecycle of software projects. Our approach leverages Google Gemini 2.5 Pro model to generate, correct, and manage software components within a semi-automated and incremental workflow. AIPyCraft enables project creation, environment setup, error correction, and feature evolution in an integrated manner. We develop and test a blockchain-based Oracle component designed for 6G wireless network environments, i.e., a complex, real-world scenario that demands secure data integration and modular extensibility. Preliminary experiments demonstrate AIPyCraft’s potential to accelerate small-scale software project development through an “understand-by-building” methodology. Our findings show that using an LLM to generate effective TOML jobs for Off-chain 6G functions is feasible, with an average of 1.05 iterations to correct the TOML code and mean experiment time of 27.8 seconds.